Scoping review of COVID-19-related systematic reviews and meta-analyses: can we really have confidence in their results?

Postgrad Med J. 2022 May;98(1159):372-379. doi: 10.1136/postgradmedj-2020-139392. Epub 2021 Feb 26.

Abstract

Aim: The aim of this study was to systematically appraise the quality of a sample of COVID-19-related systematic reviews (SRs) and discuss internal validity threats affecting the COVID-19 body of evidence.

Design: We conducted a scoping review of the literature. SRs with or without meta-analysis (MA) that evaluated clinical data, outcomes or treatments for patients with COVID-19 were included.

Main outcome measures: We extracted quality characteristics guided by A Measurement Tool to Assess Systematic Reviews-2 to calculate a qualitative score. Complementary evaluation of the most prominent published limitations affecting the COVID-19 body of evidence was performed.

Results: A total of 63 SRs were included. The majority were judged as a critically low methodological quality. Most of the studies were not guided by a pre-established protocol (39, 62%). More than half (39, 62%) failed to address risk of bias when interpreting their results. A comprehensive literature search strategy was reported in most SRs (54, 86%). Appropriate use of statistical methods was evident in nearly all SRs with MAs (39, 95%). Only 16 (33%) studies recognised heterogeneity in the definition of severe COVID-19 as a limitation of the study, and 15 (24%) recognised repeated patient populations as a limitation.

Conclusion: The methodological and reporting quality of current COVID-19 SR is far from optimal. In addition, most of the current SRs fail to address relevant threats to their internal validity, including repeated patients and heterogeneity in the definition of severe COVID-19. Adherence to proper study design and peer-review practices must remain to mitigate current limitations.

Keywords: AMSTAR-2; COVID-19; SARS-CoV-2; quality; systematic reviews.

Publication types

  • Review

MeSH terms

  • Bias
  • COVID-19* / epidemiology
  • Humans
  • Research Design